In [1]:
import cv2
import numpy as np
import tensorflow as tf
from scipy.misc import imread
import matplotlib.pyplot as plt
import operator
import itertools
import pickle
from ssd import SSD300
from ssd_utils import BBoxUtility
from sklearn.metrics import confusion_matrix, precision_score, recall_score, roc_auc_score, \
classification_report, precision_recall_curve, average_precision_score
from keras.utils import np_utils
from keras.preprocessing import image
from keras.backend.tensorflow_backend import set_session
from keras.applications.imagenet_utils import preprocess_input
%matplotlib inline
plt.rcParams['figure.figsize'] = (6, 6)
plt.rcParams['image.interpolation'] = 'nearest'
/home/agemono/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/agemono/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/agemono/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/agemono/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/agemono/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/agemono/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
/home/agemono/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/agemono/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/agemono/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/agemono/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/agemono/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/agemono/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
Bad key "text.kerning_factor" on line 4 in
/home/agemono/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.
You probably need to get an updated matplotlibrc file from
https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template
or from the matplotlib source distribution
Using TensorFlow backend.
In [6]:
NUM_CLASSES = 3 # 1 added to include background
CLASSES = ['car', 'pedestrian']
input_shape = (300, 300, 3)
In [7]:
gt = pickle.load(open('groundtruth-encoded.pkl', 'rb'))
base_path = 'checkpoints-nobackground/'
# predictions = pickle.load(open(base_path + 'predictions.pkl', 'rb'))
priors = pickle.load(open('prior_boxes_ssd300.pkl', 'rb'))
bbox_util = BBoxUtility(NUM_CLASSES, priors)
In [8]:
# format [xmin, ymin, xmax, ymax, encoded_y]
gt['object-detection-crowdai/1479505369437332886.jpg']
Out[8]:
array([[ 0. , 0.48666667, 0.11354167, 0.73583333, 1. ,
0. ],
[ 0.084375 , 0.495 , 0.221875 , 0.66833333, 1. ,
0. ],
[ 0.1984375 , 0.49083333, 0.29947917, 0.615 , 1. ,
0. ],
[ 0.275 , 0.4825 , 0.34270833, 0.58833333, 1. ,
0. ],
[ 0.66822917, 0.47416667, 0.83802083, 0.645 , 1. ,
0. ],
[ 0.59583333, 0.47916667, 0.6828125 , 0.57583333, 1. ,
0. ],
[ 0.56458333, 0.485 , 0.61145833, 0.55 , 1. ,
0. ],
[ 0.82760417, 0.4375 , 0.9859375 , 0.57333333, 1. ,
0. ],
[ 0.89322917, 0.44416667, 1. , 0.57166667, 1. ,
0. ],
[ 0.29375 , 0.47583333, 0.36197917, 0.56666667, 1. ,
0. ],
[ 0.353125 , 0.49083333, 0.38125 , 0.5625 , 1. ,
0. ],
[ 0.36770833, 0.48583333, 0.40260417, 0.54666667, 1. ,
0. ],
[ 0.546875 , 0.485 , 0.56875 , 0.51583333, 1. ,
0. ],
[ 0.44166667, 0.48666667, 0.46614583, 0.52083333, 1. ,
0. ],
[ 0.39270833, 0.49416667, 0.41875 , 0.5425 , 1. ,
0. ],
[ 0.40833333, 0.49416667, 0.42916667, 0.53333333, 1. ,
0. ]])
In [5]:
predictions
Out[5]:
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In [4]:
pred_prob = predictions['pred_prob']
train_keys = predictions['train_keys']
val_keys = predictions['val_keys']
In [5]:
def detection_out(predictions, background_label_id=0, keep_top_k=200, confidence_threshold=0.01):
mbox_loc = predictions[:, :, :4]
variances = predictions[:, :, -4:]
mbox_priorbox = predictions[:, :, -8:-4]
mbox_conf = predictions[:, :, 4:-8]
results = []
scores = []
# print('len(mbox_loc)', len(mbox_loc))
# print('len(variances)', len(variances))
# print('len(mbox_priorbox)', len(mbox_priorbox))
# print('len(mbox_conf)', len(mbox_conf))
for i in range(len(mbox_loc)):
results.append([])
scores.append([])
decode_bbox = bbox_util.decode_boxes(mbox_loc[i],
mbox_priorbox[i], variances[i])
for c in range(bbox_util.num_classes):
if c == background_label_id:
continue
c_confs = mbox_conf[i, :, c]
all_confs = mbox_conf[i, :, :]
c_confs_m = c_confs > confidence_threshold
if len(c_confs[c_confs_m]) > 0:
boxes_to_process = decode_bbox[c_confs_m]
confs_to_process = c_confs[c_confs_m]
all_confs_to_process = all_confs[c_confs_m]
feed_dict = {bbox_util.boxes: boxes_to_process,
bbox_util.scores: confs_to_process}
idx = bbox_util.sess.run(bbox_util.nms, feed_dict=feed_dict)
good_boxes = boxes_to_process[idx]
confs = confs_to_process[idx][:, None]
equal = np.all(mbox_conf[i, :, :] == mbox_conf[i, ::])
if not equal:
raise ValueError('FUCKKKKKKKKKKKKK')
labels = c * np.ones((len(idx), 1))
c_pred = np.concatenate((labels, confs, good_boxes),
axis=1)
scores[-1].extend(all_confs_to_process[idx])
results[-1].extend(c_pred)
if len(results[-1]) > 0:
results[-1] = np.array(results[-1])
scores[-1] = np.array(scores[-1])
argsort = np.argsort(results[-1][:, 1])[::-1]
results[-1] = results[-1][argsort]
results[-1] = results[-1][:keep_top_k]
scores[-1] = scores[-1][argsort]
scores[-1] = scores[-1][:keep_top_k]
# print('final scores[-1]', scores[-1])
# print('final results[-1]', results[-1])
return results, scores
results, scores = detection_out(pred_prob, keep_top_k=50, confidence_threshold=0.7)
In [8]:
# format: [label, confidence, bounding box points]
results
Out[8]:
[array([[ 1. , 0.99947029, 0. , 0.3866452 , 0.17504175,
0.59145033],
[ 1. , 0.99860018, 0.34830108, 0.46173498, 0.44396725,
0.57625878],
[ 1. , 0.711788 , 0.44432065, 0.4747752 , 0.47123423,
0.52646077],
[ 1. , 0.70360947, 0.42448345, 0.46051693, 0.45959899,
0.52580148]]),
array([[ 1. , 0.99661177, 0.10990873, 0.45457557, 0.21244872,
0.55732369],
[ 1. , 0.72310698, 0.25646028, 0.4610554 , 0.2870076 ,
0.4998256 ],
[ 1. , 0.71259111, 0.23009229, 0.45833358, 0.2647047 ,
0.50416535],
[ 2. , 0.71015859, 0. , 0.47594178, 0.01810253,
0.55500829],
[ 1. , 0.70932305, 0.21178839, 0.47768554, 0.25782362,
0.52939624],
[ 1. , 0.70801419, 0.07018723, 0.47626591, 0.1324522 ,
0.55257499],
[ 1. , 0.70500469, 0.2561931 , 0.49162596, 0.28597969,
0.53088087]]),
array([[ 1. , 0.99967504, 0.31867334, 0.46650779, 0.40667942,
0.57441986],
[ 1. , 0.9986524 , 0. , 0.32636929, 0.16592719,
0.79448771],
[ 1. , 0.99596381, 0.62671441, 0.47330621, 0.74120349,
0.5881868 ],
[ 1. , 0.95588523, 0.43723115, 0.45989862, 0.55211198,
0.61342734],
[ 1. , 0.87984025, 0.93348497, 0.47819746, 1. ,
0.74246287],
[ 1. , 0.75266683, 0.18322998, 0.47819817, 0.25046033,
0.54668891],
[ 1. , 0.7262609 , 0.17244805, 0.4869321 , 0.22062205,
0.54140824],
[ 1. , 0.71951598, 0.27950776, 0.47606629, 0.31762093,
0.52083659],
[ 1. , 0.71475613, 0.24594317, 0.48039711, 0.29605705,
0.52924919],
[ 1. , 0.7035324 , 0.14670603, 0.49126819, 0.18202449,
0.53228164],
[ 1. , 0.70318937, 0.27307487, 0.49127975, 0.30410409,
0.53089327]]),
array([[ 1. , 0.99407625, 0.80492502, 0.46771765, 1. ,
0.68173599],
[ 1. , 0.98612493, 0.05010421, 0.47501439, 0.16892916,
0.58586413],
[ 1. , 0.93385994, 0.16042621, 0.47584185, 0.23344515,
0.56213486],
[ 1. , 0.89091444, 0.66201973, 0.44222546, 0.81109214,
0.61371958],
[ 1. , 0.84582371, 0. , 0.46495458, 0.04834491,
0.60739613],
[ 1. , 0.79293984, 0.61353213, 0.46396908, 0.68475527,
0.54726893],
[ 2. , 0.79087347, 0.75169331, 0.41715676, 0.77311307,
0.44849849],
[ 2. , 0.77505237, 0.27322096, 0.45529547, 0.29338086,
0.53117031],
[ 2. , 0.77286559, 0.78651047, 0.44025305, 0.82322681,
0.54394978],
[ 2. , 0.74970359, 0.77800262, 0.41678545, 0.81405377,
0.5126856 ],
[ 1. , 0.72911239, 0.94456166, 0.4877508 , 1. ,
0.58325696],
[ 2. , 0.72546536, 0.2876122 , 0.4581328 , 0.30951536,
0.52620858],
[ 2. , 0.71635282, 0.79720235, 0.48053932, 0.82926178,
0.57585335],
[ 1. , 0.7148844 , 0.87902075, 0.46477741, 1. ,
0.57901889],
[ 2. , 0.71249539, 0.7764973 , 0.4027245 , 0.80353302,
0.46980512]]),
array([[ 3. , 0.996521 , 0.61530715, 0.31816253, 0.76499528,
0.57547355],
[ 1. , 0.72619134, 0. , 0.46915346, 0.04178021,
0.51850969]]),
array([[ 1.00000000e+00, 9.92950320e-01, 2.64257163e-01,
4.36457038e-01, 4.24100548e-01, 6.19140506e-01],
[ 1.00000000e+00, 9.66927767e-01, 4.15230691e-01,
4.66572285e-01, 4.87055361e-01, 5.55001378e-01],
[ 1.00000000e+00, 8.11074376e-01, 2.08877027e-05,
5.30197561e-01, 6.25695586e-02, 7.95230925e-01],
[ 3.00000000e+00, 7.86578953e-01, 8.02837908e-01,
3.84166718e-01, 9.27392781e-01, 6.21079803e-01],
[ 1.00000000e+00, 7.53273249e-01, 4.79928195e-01,
4.70425367e-01, 5.19647539e-01, 5.33729911e-01],
[ 3.00000000e+00, 7.50018418e-01, 7.67046750e-01,
4.16952193e-01, 8.81934464e-01, 6.59324706e-01],
[ 3.00000000e+00, 7.24972665e-01, 8.70204091e-01,
3.84984314e-01, 9.96632099e-01, 6.18196666e-01],
[ 1.00000000e+00, 7.13437319e-01, 5.17043352e-01,
4.81634289e-01, 5.40996313e-01, 5.23051143e-01],
[ 1.00000000e+00, 7.04139829e-01, 4.93867874e-01,
4.76210833e-01, 5.23132205e-01, 5.22756457e-01]]),
array([[ 1. , 0.9999702 , 0.39480227, 0.42517799, 0.59478688,
0.68379861],
[ 1. , 0.96095824, 0.86302125, 0.40275851, 1. ,
0.79780841],
[ 1. , 0.7643075 , 0.31906128, 0.45416138, 0.37279904,
0.52581996],
[ 1. , 0.75682777, 0.88213164, 0.42008874, 0.95338017,
0.4947972 ],
[ 1. , 0.7437005 , 0.23212001, 0.46549547, 0.31779519,
0.524764 ],
[ 1. , 0.74244082, 0.3621943 , 0.44437343, 0.39190489,
0.50148755],
[ 1. , 0.73322141, 0.34170914, 0.43907252, 0.37862724,
0.50659579],
[ 1. , 0.73275685, 0.56996077, 0.4404709 , 0.61151892,
0.49980834],
[ 1. , 0.72029763, 0.57671285, 0.45332792, 0.63928485,
0.50513333],
[ 1. , 0.71570212, 0.87863725, 0.4236249 , 0.92976767,
0.47513363],
[ 1. , 0.71450287, 0.36173773, 0.47121811, 0.39087319,
0.52637142],
[ 1. , 0.7128939 , 0.37878552, 0.45866844, 0.40491161,
0.50317162],
[ 1. , 0.70900947, 0.38983202, 0.46288237, 0.41555291,
0.51193249],
[ 1. , 0.70637006, 0.30732319, 0.46668911, 0.35428318,
0.53906691]]),
array([[ 1. , 0.99788743, 0.73892951, 0.45333636, 1. ,
0.64870858],
[ 1. , 0.9500047 , 0.05981901, 0.44047713, 0.22195384,
0.53752577],
[ 1. , 0.85924554, 0.62288082, 0.44202575, 0.79275036,
0.58319491],
[ 1. , 0.7573095 , 0.51742476, 0.42698869, 0.66094428,
0.58618671],
[ 1. , 0.74951339, 0.76884627, 0.45969212, 0.86241579,
0.52658617],
[ 1. , 0.74424356, 0.23189285, 0.46975854, 0.29145905,
0.52970606],
[ 1. , 0.73555815, 0.20089634, 0.45863831, 0.2509996 ,
0.52910233],
[ 1. , 0.73441201, 0.54233581, 0.44365266, 0.58775932,
0.50389928],
[ 1. , 0.73270679, 0.46032324, 0.46132395, 0.51774043,
0.53289032],
[ 1. , 0.72962177, 0.69961417, 0.44075206, 0.79586411,
0.53853208],
[ 1. , 0.7223655 , 0.28228882, 0.48666668, 0.32178888,
0.52624536],
[ 1. , 0.72220439, 0.43181634, 0.44276559, 0.57246554,
0.59707618],
[ 1. , 0.71951705, 0.24547105, 0.45174077, 0.29838148,
0.50522172],
[ 1. , 0.71221495, 0.73976171, 0.4530451 , 0.82152772,
0.52213752],
[ 1. , 0.7109344 , 0.31262991, 0.48935497, 0.34472993,
0.5240413 ],
[ 1. , 0.70461142, 0.56916064, 0.44546428, 0.61149317,
0.50307399],
[ 2. , 0.70327675, 0.47496775, 0.38391066, 0.53842199,
0.75050151]]),
array([[ 1. , 0.99781597, 0.15592352, 0.47306216, 0.2464239 ,
0.57342947],
[ 1. , 0.99148703, 0.70145732, 0.43273288, 0.87945312,
0.62614268],
[ 1. , 0.98951262, 0.29213855, 0.47290334, 0.36648563,
0.55638236],
[ 1. , 0.98034781, 0.61544275, 0.46292186, 0.70165741,
0.57791793],
[ 1. , 0.78437263, 0.23196666, 0.47957814, 0.26696301,
0.55360413],
[ 1. , 0.78211421, 0.27273595, 0.4664847 , 0.3115105 ,
0.5271681 ],
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0.55036223],
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4.58699822e-01, 3.20054173e-01, 5.53624272e-01],
[ 1.00000000e+00, 7.13365078e-01, 8.45656097e-01,
4.66788977e-01, 9.64630067e-01, 6.20614111e-01],
[ 1.00000000e+00, 7.07883418e-01, 2.58413889e-02,
4.43959564e-01, 1.05467290e-01, 5.50145864e-01]]),
array([[ 1. , 0.99442589, 0.5369758 , 0.49625295, 0.6116423 ,
0.5903185 ],
[ 1. , 0.99089819, 0.35759676, 0.47787035, 0.44838035,
0.60301316],
[ 1. , 0.90292531, 0.60519171, 0.5027374 , 0.73073292,
0.64707077],
[ 1. , 0.85623473, 0.81583053, 0.47572124, 0.95045215,
0.57963693],
[ 1. , 0.78126609, 0. , 0.50408292, 0.06013113,
0.58688128],
[ 1. , 0.76666009, 0.40869537, 0.4882634 , 0.46098486,
0.58322108],
[ 1. , 0.74825013, 0.43790761, 0.48686692, 0.47581378,
0.5632565 ],
[ 1. , 0.72626674, 0.50718582, 0.50391203, 0.55277812,
0.57396036],
[ 1. , 0.71936309, 0.45350561, 0.5072298 , 0.48803952,
0.5658977 ],
[ 1. , 0.70570189, 0.43521237, 0.51474804, 0.47505158,
0.59404308]]),
array([[ 1. , 0.94839531, 0.00611963, 0.48224562, 0.11605974,
0.57699674],
[ 1. , 0.88706684, 0.12413734, 0.48346987, 0.20734271,
0.55808568],
[ 1. , 0.76247674, 0.18901432, 0.4756299 , 0.25939852,
0.54494607],
[ 1. , 0.74315083, 0.25355902, 0.47039494, 0.29034147,
0.52292287],
[ 1. , 0.73955894, 0.16055767, 0.47931641, 0.2358752 ,
0.55182117],
[ 1. , 0.73302639, 0.32387838, 0.49387568, 0.36741838,
0.53177243],
[ 1. , 0.72762436, 0.23248775, 0.48723605, 0.27957293,
0.52888036],
[ 1. , 0.72485793, 0.18359667, 0.48627076, 0.23525372,
0.52608776],
[ 1. , 0.71711349, 0.28171876, 0.48788083, 0.31964502,
0.52842486],
[ 1. , 0.7168014 , 0.31551176, 0.47240511, 0.36312616,
0.51441664],
[ 1. , 0.71399593, 0.25696009, 0.48899698, 0.29872745,
0.53085637],
[ 1. , 0.70120102, 0.45625782, 0.47317994, 0.49066424,
0.52069658]]),
array([[ 1. , 0.99860865, 0.38324627, 0.49624848, 0.47437915,
0.62471831]]),
array([[ 1. , 0.99992847, 0.03213029, 0.44146851, 0.2989912 ,
0.69579315],
[ 1. , 0.98131406, 0.48843774, 0.46882468, 0.55985844,
0.56338149],
[ 1. , 0.97468865, 0.72328663, 0.43881318, 0.94063175,
0.56667459],
[ 1. , 0.71565986, 0.25913766, 0.46221536, 0.32199582,
0.53395814],
[ 1. , 0.7015729 , 0.91259378, 0.43387941, 1. ,
0.54001182]]),
array([[ 1. , 0.92098492, 0.48526946, 0.43872482, 0.61075592,
0.58521789]]),
array([[ 1. , 0.93030369, 0.52342898, 0.48373058, 0.6422711 ,
0.61774093]]),
array([[ 1. , 0.978576 , 0.40047529, 0.47286385, 0.52100241,
0.55830473],
[ 1. , 0.78574806, 0.10554025, 0.467089 , 0.23156616,
0.60852629],
[ 1. , 0.75497276, 0.60947448, 0.48435694, 0.74220163,
0.61126059]]),
array([[ 1. , 0.97085226, 0.16569275, 0.49524084, 0.2781229 ,
0.62073767]]),
array([[ 1. , 0.99999964, 0.16507536, 0.44066626, 0.49165577,
0.85862714],
[ 1. , 0.98941994, 0.75719428, 0.45791659, 0.99271274,
0.58674955],
[ 1. , 0.88313937, 0.08311484, 0.50532472, 0.15929264,
0.58059168],
[ 1. , 0.79911762, 0.75085306, 0.50933534, 0.98923624,
0.63738662],
[ 2. , 0.76283139, 0.18918484, 0.48713127, 0.2143676 ,
0.55435145],
[ 2. , 0.74297041, 0.16304132, 0.48444632, 0.18771267,
0.5593276 ]]),
array([[ 3.00000000e+00, 9.99902964e-01, 7.16590226e-01,
2.53251195e-03, 1.00000000e+00, 7.17754006e-01],
[ 1.00000000e+00, 9.99638677e-01, 1.59581989e-01,
3.02428365e-01, 6.60579681e-01, 9.56917048e-01],
[ 1.00000000e+00, 8.39734316e-01, 6.75513804e-01,
4.53132570e-01, 7.30546892e-01, 6.14919364e-01],
[ 3.00000000e+00, 7.38315463e-01, 6.94560468e-01,
2.96118557e-01, 8.33758771e-01, 6.13856316e-01]]),
array([[ 1. , 0.71419829, 0. , 0.42648229, 0.06038693,
0.78912878]]),
array([[ 1. , 0.87933052, 0.14004272, 0.4563033 , 0.28653121,
0.6114381 ]]),
[],
array([[ 1. , 0.99883145, 0.21281204, 0.45605776, 0.29888937,
0.55983245],
[ 1. , 0.9705258 , 0. , 0.49613231, 0.08842684,
0.57101053],
[ 1. , 0.91720414, 0.89191687, 0.44087008, 1. ,
0.49890587],
[ 1. , 0.7837615 , 0.12954742, 0.49234065, 0.20194262,
0.56114817],
[ 1. , 0.72807819, 0.8582173 , 0.45176023, 0.93874973,
0.49203771]]),
array([[ 1. , 0.99801648, 0.43251294, 0.45237601, 0.50521082,
0.55168843]]),
array([[ 1. , 0.95220834, 0.06799666, 0.44322911, 0.16570762,
0.54181087],
[ 3. , 0.95166302, 0.76487005, 0.29267377, 0.9749999 ,
0.59681129]]),
[],
array([[ 1. , 0.83112758, 0.85060918, 0.43202716, 0.98769808,
0.57074338]]),
array([[ 1. , 0.8834303 , 0.69565117, 0.48165336, 0.87803423,
0.57713574]]),
array([[ 1. , 0.9630152 , 0.26864561, 0.49693513, 0.33797708,
0.58064055]]),
array([[ 1. , 0.91449153, 0.7945081 , 0.47545218, 0.92498624,
0.56822455],
[ 1. , 0.70362282, 0.90508997, 0.47065625, 1. ,
0.5923602 ]]),
array([[ 1. , 0.99795073, 0.18018997, 0.46755099, 0.33959752,
0.63310301],
[ 1. , 0.93773752, 0.75460625, 0.4484207 , 0.90237224,
0.54170924],
[ 1. , 0.75608528, 0.72148097, 0.4581556 , 0.7974453 ,
0.52093399],
[ 1. , 0.7286219 , 0.6950503 , 0.45895219, 0.76501578,
0.52169985],
[ 1. , 0.71053052, 0.74806124, 0.46647474, 0.86176592,
0.51845253],
[ 1. , 0.70971525, 0.74083912, 0.43970069, 0.81202924,
0.5070312 ],
[ 1. , 0.70199353, 0.70381868, 0.44913703, 0.75394404,
0.50185859]]),
array([[ 1. , 0.98457056, 0.5256896 , 0.45737907, 0.63772726,
0.58430284]]),
array([[ 1. , 0.99981266, 0.73085308, 0.45946288, 0.94093561,
0.64117765],
[ 1. , 0.99298567, 0.48364389, 0.44644603, 0.56047285,
0.55404705],
[ 1. , 0.80724102, 0. , 0.45431778, 0.09414892,
0.58113045]]),
array([[ 1. , 0.95172167, 0.80414754, 0.43542975, 1. ,
0.54139274]]),
array([[ 1. , 0.99957222, 0.74661541, 0.37358949, 1. ,
0.72246861]]),
array([[ 1. , 0.99997246, 0.62563372, 0.3403194 , 1. ,
0.9179337 ],
[ 1. , 0.98357892, 0.12611878, 0.5300315 , 0.22944576,
0.62915224],
[ 1. , 0.90318239, 0.57663989, 0.48769957, 0.66406775,
0.65225416],
[ 1. , 0.83535957, 0. , 0.492401 , 0.04522946,
0.69266784],
[ 1. , 0.80916524, 0.542427 , 0.50342661, 0.60025626,
0.62111193],
[ 1. , 0.78539252, 0.58580035, 0.45669678, 0.69801086,
0.72530758],
[ 1. , 0.76882201, 0.06713437, 0.53267282, 0.12992746,
0.5929777 ],
[ 1. , 0.76549423, 0.1056801 , 0.50911862, 0.25505176,
0.66256851],
[ 1. , 0.75848114, 0.03690726, 0.53367871, 0.10302532,
0.60435623],
[ 1. , 0.74994469, 0.51472706, 0.50574088, 0.57221061,
0.58812916],
[ 1. , 0.74495292, 0.62061995, 0.48605552, 0.71121806,
0.66976309],
[ 1. , 0.74254596, 0.01081608, 0.52721691, 0.05790417,
0.64564657],
[ 2. , 0.73013622, 0.1144751 , 0.50562036, 0.13999921,
0.5946399 ],
[ 1. , 0.72953361, 0.04370002, 0.55122721, 0.07344865,
0.61362278],
[ 1. , 0.72817093, 0.61749858, 0.39536536, 0.7753467 ,
0.80984187],
[ 2. , 0.71905226, 0.10360976, 0.53837526, 0.13344535,
0.63060355],
[ 1. , 0.7156086 , 0.0245195 , 0.52491492, 0.0755114 ,
0.60894555],
[ 1. , 0.70762664, 0.55901796, 0.47616625, 0.61694008,
0.58821583],
[ 1. , 0.70601636, 0.49504584, 0.50451803, 0.51157385,
0.53113711]]),
array([[ 1. , 0.99999249, 0.3490603 , 0.35754219, 0.73672807,
0.87730455],
[ 1. , 0.99000484, 0.72532111, 0.45837584, 0.80770272,
0.57433176],
[ 1. , 0.98591667, 0.86465597, 0.40265647, 1. ,
0.61819226],
[ 1. , 0.88071656, 0.79429835, 0.4262135 , 0.90158218,
0.5915724 ],
[ 1. , 0.78841066, 0.33846664, 0.44275728, 0.36897105,
0.49638811],
[ 1. , 0.76113057, 0.34103757, 0.42831489, 0.37223983,
0.47793368],
[ 1. , 0.75849658, 0.77478939, 0.44651338, 0.81765193,
0.54031515],
[ 3. , 0.7492758 , 0.89958096, 0.21249583, 1. ,
0.48587582],
[ 1. , 0.74374604, 0.32884252, 0.45246464, 0.36108947,
0.50434482],
[ 1. , 0.73630071, 0.3182537 , 0.43012232, 0.34880847,
0.476542 ],
[ 1. , 0.71518207, 0.29759106, 0.45165354, 0.33831945,
0.50637549],
[ 1. , 0.71172464, 0.36244223, 0.45955026, 0.39019814,
0.50198823],
[ 1. , 0.71081632, 0.8529774 , 0.45642227, 0.91271853,
0.60322875],
[ 2. , 0.70896161, 0.74141014, 0.46264777, 0.75548327,
0.49098352],
[ 1. , 0.70512241, 0.73928332, 0.47151271, 0.77895868,
0.5265767 ],
[ 1. , 0.70395535, 0.75582176, 0.47147036, 0.80879015,
0.53998017]]),
array([[ 1. , 0.99268931, 0.21350943, 0.45554674, 0.60099453,
0.67475188],
[ 1. , 0.91948479, 0.13528204, 0.50321746, 0.21751326,
0.60257351],
[ 2. , 0.78698218, 0.58040249, 0.45938948, 0.60884857,
0.56582075],
[ 2. , 0.7765066 , 0.56358266, 0.45641616, 0.58817327,
0.53308916],
[ 1. , 0.75862187, 0.65475076, 0.46186575, 0.89574367,
0.60155618],
[ 2. , 0.74361765, 0.49118727, 0.45350069, 0.52778476,
0.53359711],
[ 2. , 0.74151611, 0.60151434, 0.4602997 , 0.62732494,
0.56979334],
[ 2. , 0.72942287, 0.49663961, 0.44210526, 0.52299654,
0.50664878],
[ 2. , 0.72366261, 0.59658772, 0.45183238, 0.61991483,
0.54062778],
[ 2. , 0.71856225, 0.47926724, 0.44795138, 0.51098371,
0.53501987],
[ 2. , 0.7104404 , 0.73864657, 0.47032815, 0.77367109,
0.54301947],
[ 2. , 0.71029961, 0.62511963, 0.46804526, 0.64721006,
0.56303298],
[ 2. , 0.70821118, 0.52767372, 0.48985472, 0.56229055,
0.54429007],
[ 2. , 0.70310497, 0.53127706, 0.45845696, 0.56227517,
0.53103077],
[ 2. , 0.70276999, 0.5418306 , 0.47539005, 0.56792027,
0.52777004],
[ 2. , 0.70108247, 0.63177198, 0.4506304 , 0.65336901,
0.53879297]]),
array([[ 3.00000000e+00, 9.98651206e-01, 3.37428987e-01,
3.77406538e-01, 4.78227556e-01, 6.39877737e-01],
[ 1.00000000e+00, 9.88238931e-01, 7.32332468e-04,
4.53543335e-01, 1.92755699e-01, 7.57891536e-01],
[ 1.00000000e+00, 7.93406546e-01, 9.46954191e-02,
5.09248614e-01, 1.94465995e-01, 6.08261466e-01],
[ 1.00000000e+00, 7.47125268e-01, 1.29216313e-01,
5.29752135e-01, 2.16404468e-01, 6.18391752e-01]]),
[],
array([[ 3. , 0.99961036, 0.64147156, 0.21558739, 1. ,
0.63932633],
[ 1. , 0.86219198, 0.50035083, 0.44642013, 0.56373644,
0.53663611],
[ 1. , 0.8603245 , 0.26765776, 0.43638819, 0.40616173,
0.5915783 ],
[ 1. , 0.79317307, 0.41932544, 0.45838797, 0.48122081,
0.53673118],
[ 3. , 0.79002213, 0.39452574, 0.42003253, 0.44431534,
0.47642937],
[ 1. , 0.75204313, 0.55803192, 0.46158808, 0.58276033,
0.49813521],
[ 1. , 0.75203574, 0.46662286, 0.45826593, 0.50618243,
0.51754165],
[ 1. , 0.74787652, 0.49012244, 0.45444712, 0.53446841,
0.52557558],
[ 1. , 0.742697 , 0.52680165, 0.46163464, 0.55876261,
0.50941324],
[ 1. , 0.72502416, 0. , 0.48762789, 0.04083906,
0.53338951],
[ 1. , 0.71839881, 0.5848307 , 0.46120292, 0.61970633,
0.50889432],
[ 1. , 0.71817958, 0.48687845, 0.44577238, 0.52001506,
0.50080782],
[ 1. , 0.71181464, 0.47186962, 0.48947415, 0.50800347,
0.54529709],
[ 1. , 0.71009928, 0.43981209, 0.44670582, 0.4773123 ,
0.50729144],
[ 1. , 0.70366001, 0.44992527, 0.46510902, 0.48681954,
0.51855046],
[ 1. , 0.70327413, 0.56002867, 0.47886503, 0.585338 ,
0.51933885],
[ 1. , 0.70011932, 0.54847282, 0.4792017 , 0.57361144,
0.51365 ]]),
array([[ 1. , 0.99663866, 0.456404 , 0.41491345, 0.53600651,
0.55615675],
[ 1. , 0.77318233, 0. , 0.44125786, 0.06498887,
0.58981562]]),
array([[ 1. , 0.99988425, 0.23315004, 0.41789639, 0.4459292 ,
0.72845531],
[ 1. , 0.73326391, 0.93385267, 0.45943868, 1. ,
0.52755284]]),
...]
In [9]:
len(results[0])
Out[9]:
4
In [10]:
# probability of each class, 0 indicating probability of background
scores
Out[10]:
[array([[ 2.91416713e-04, 9.99470294e-01, 7.00410681e-07,
2.37608430e-04],
[ 1.39974477e-03, 9.98600185e-01, 3.26897731e-09,
9.06392827e-08],
[ 2.84721315e-01, 7.11787999e-01, 6.17797021e-04,
2.87293224e-03],
[ 2.96261013e-01, 7.03609467e-01, 7.42542616e-05,
5.52879064e-05]], dtype=float32),
array([[ 3.35948705e-03, 9.96611774e-01, 4.91398964e-07,
2.82241344e-05],
[ 2.76233733e-01, 7.23106980e-01, 1.13749185e-04,
5.45513583e-04],
[ 2.85624027e-01, 7.12591112e-01, 1.73733017e-04,
1.61121308e-03],
[ 2.73700178e-01, 1.59294941e-02, 7.10158587e-01,
2.11730483e-04],
[ 2.89782703e-01, 7.09323049e-01, 4.79295035e-04,
4.15008370e-04],
[ 2.91865766e-01, 7.08014190e-01, 1.10139597e-04,
9.96955077e-06],
[ 2.91355520e-01, 7.05004692e-01, 7.03832367e-04,
2.93593854e-03]], dtype=float32),
array([[ 3.24588531e-04, 9.99675035e-01, 1.11714815e-09,
3.07943395e-07],
[ 1.33429724e-03, 9.98652399e-01, 2.13342299e-09,
1.33912099e-05],
[ 4.03615413e-03, 9.95963812e-01, 5.93430087e-08,
3.43870497e-08],
[ 4.41058241e-02, 9.55885231e-01, 3.74104957e-06,
5.11240887e-06],
[ 1.20098524e-01, 8.79840255e-01, 3.28379429e-06,
5.80317246e-05],
[ 2.45404467e-01, 7.52666831e-01, 8.17271124e-04,
1.11142814e-03],
[ 2.65952647e-01, 7.26260900e-01, 4.07463778e-03,
3.71183641e-03],
[ 2.80391395e-01, 7.19515979e-01, 6.54213873e-05,
2.71699028e-05],
[ 2.84392416e-01, 7.14756131e-01, 7.06268242e-04,
1.45197162e-04],
[ 2.86246955e-01, 7.03532398e-01, 8.84259492e-03,
1.37802621e-03],
[ 2.91227788e-01, 7.03189373e-01, 5.24098845e-03,
3.41810432e-04]], dtype=float32),
array([[ 5.85846184e-03, 9.94076252e-01, 6.03473200e-05,
4.96593157e-06],
[ 1.38727715e-02, 9.86124933e-01, 1.29648004e-06,
9.20638797e-07],
[ 6.60723373e-02, 9.33859944e-01, 3.90780770e-05,
2.85584956e-05],
[ 1.09053120e-01, 8.90914440e-01, 3.09998104e-05,
1.47674757e-06],
[ 1.53143257e-01, 8.45823705e-01, 9.50214569e-04,
8.28266348e-05],
[ 2.05509022e-01, 7.92939842e-01, 1.47097115e-03,
8.01823116e-05],
[ 1.87800944e-01, 2.09465977e-02, 7.90873468e-01,
3.79024830e-04],
[ 1.89741164e-01, 3.45815979e-02, 7.75052369e-01,
6.24839857e-04],
[ 1.58938855e-01, 6.65543005e-02, 7.72865593e-01,
1.64128153e-03],
[ 2.33609170e-01, 1.60483569e-02, 7.49703586e-01,
6.38914993e-04],
[ 2.63383359e-01, 7.29112387e-01, 4.92425729e-03,
2.57997960e-03],
[ 1.77554041e-01, 9.57681835e-02, 7.25465357e-01,
1.21252087e-03],
[ 1.97952777e-01, 8.47626403e-02, 7.16352820e-01,
9.31713905e-04],
[ 2.82750279e-01, 7.14884400e-01, 2.04194267e-03,
3.23340239e-04],
[ 2.76345670e-01, 1.06966943e-02, 7.12495387e-01,
4.62222291e-04]], dtype=float32),
array([[ 3.47113307e-03, 7.84528038e-06, 5.17482368e-09,
9.96520996e-01],
[ 2.73202538e-01, 7.26191342e-01, 5.63544454e-04,
4.26159349e-05]], dtype=float32),
array([[ 7.04571977e-03, 9.92950320e-01, 1.72715056e-06,
2.30683600e-06],
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[ 7.00961100e-03, 9.92985666e-01, 1.09463860e-08,
4.69397082e-06],
[ 1.92748472e-01, 8.07241023e-01, 3.17848139e-06,
7.28526766e-06]], dtype=float32),
array([[ 4.82776389e-02, 9.51721668e-01, 6.35546826e-07,
2.80224182e-08]], dtype=float32),
array([[ 4.21817997e-04, 9.99572217e-01, 7.10990017e-11,
5.98447423e-06]], dtype=float32),
array([[ 2.59313583e-05, 9.99972463e-01, 3.17072655e-14,
1.51502195e-06],
[ 1.63989943e-02, 9.83578920e-01, 4.52311133e-06,
1.76128469e-05],
[ 9.67935100e-02, 9.03182387e-01, 2.39799010e-05,
1.40238967e-07],
[ 1.64435923e-01, 8.35359573e-01, 2.00272712e-04,
4.28520343e-06],
[ 1.90761417e-01, 8.09165239e-01, 7.27503430e-05,
5.83112126e-07],
[ 2.14583382e-01, 7.85392523e-01, 1.69328687e-05,
7.13693134e-06],
[ 2.25639924e-01, 7.68822014e-01, 5.36477566e-03,
1.73285443e-04],
[ 2.23255768e-01, 7.65494227e-01, 5.11842081e-03,
6.13154471e-03],
[ 2.32298717e-01, 7.58481145e-01, 8.46909732e-03,
7.51098967e-04],
[ 2.49987736e-01, 7.49944687e-01, 6.52698800e-05,
2.34273762e-06],
[ 2.51942426e-01, 7.44952917e-01, 3.08650313e-03,
1.80961379e-05],
[ 2.56646812e-01, 7.42545962e-01, 7.92996725e-04,
1.41478349e-05],
[ 1.87288254e-01, 7.80612305e-02, 7.30136216e-01,
4.51430259e-03],
[ 2.51854241e-01, 7.29533613e-01, 1.86097324e-02,
2.39336464e-06],
[ 2.71583229e-01, 7.28170931e-01, 2.09948223e-04,
3.58211000e-05],
[ 2.02547342e-01, 7.68776909e-02, 7.19052255e-01,
1.52276130e-03],
[ 2.82246262e-01, 7.15608597e-01, 1.96230132e-03,
1.82788805e-04],
[ 2.91290104e-01, 7.07626641e-01, 1.07279653e-03,
1.04439441e-05],
[ 2.90329427e-01, 7.06016362e-01, 2.71524629e-03,
9.39044286e-04]], dtype=float32),
array([[ 7.22342429e-06, 9.99992490e-01, 2.98478554e-13,
2.63720125e-07],
[ 9.99372639e-03, 9.90004838e-01, 2.87090501e-07,
1.14752891e-06],
[ 1.40619781e-02, 9.85916674e-01, 1.05148974e-05,
1.09040966e-05],
[ 1.18516028e-01, 8.80716562e-01, 1.17870870e-04,
6.49466529e-04],
[ 1.99703723e-01, 7.88410664e-01, 8.07558745e-03,
3.81009839e-03],
[ 2.21207380e-01, 7.61130571e-01, 8.23812280e-03,
9.42401309e-03],
[ 2.26213351e-01, 7.58496583e-01, 1.20049417e-02,
3.28510767e-03],
[ 2.48298258e-01, 6.49549824e-04, 1.77639630e-03,
7.49275804e-01],
[ 2.45303109e-01, 7.43746042e-01, 1.02960365e-02,
6.54795789e-04],
[ 2.59360015e-01, 7.36300707e-01, 2.04560254e-03,
2.29368987e-03],
[ 2.81277239e-01, 7.15182066e-01, 3.20994505e-03,
3.30680225e-04],
[ 1.76480621e-01, 7.11724639e-01, 9.32564139e-02,
1.85383223e-02],
[ 2.77229249e-01, 7.10816324e-01, 1.17672682e-02,
1.87065220e-04],
[ 1.90418631e-01, 9.14401338e-02, 7.08961606e-01,
9.17962380e-03],
[ 2.74522573e-01, 7.05122411e-01, 1.86687969e-02,
1.68627407e-03],
[ 2.79946804e-01, 7.03955352e-01, 1.15715023e-02,
4.52632597e-03]], dtype=float32),
array([[ 7.29789538e-03, 9.92689312e-01, 1.08879057e-08,
1.28310894e-05],
[ 7.84894302e-02, 9.19484794e-01, 7.57907110e-05,
1.94998935e-03],
[ 2.11879492e-01, 1.07752753e-03, 7.86982179e-01,
6.07782385e-05],
[ 2.16303691e-01, 6.96125720e-03, 7.76506603e-01,
2.28496268e-04],
[ 2.41043493e-01, 7.58621871e-01, 2.25439639e-04,
1.09151566e-04],
[ 1.94289148e-01, 6.18036911e-02, 7.43617654e-01,
2.89547024e-04],
[ 2.57979155e-01, 4.80063085e-04, 7.41516113e-01,
2.46803029e-05],
[ 2.53792286e-01, 1.64689440e-02, 7.29422867e-01,
3.15908430e-04],
[ 2.75681525e-01, 5.23447583e-04, 7.23662615e-01,
1.32397254e-04],
[ 2.68386811e-01, 1.28224362e-02, 7.18562245e-01,
2.28525896e-04],
[ 2.35573471e-01, 5.36813475e-02, 7.10440397e-01,
3.04809597e-04],
[ 2.80837983e-01, 8.72467365e-03, 7.10299611e-01,
1.37737967e-04],
[ 1.76957652e-01, 1.14782356e-01, 7.08211184e-01,
4.88256410e-05],
[ 2.61066109e-01, 3.53773199e-02, 7.03104973e-01,
4.51627217e-04],
[ 2.74558485e-01, 2.24048514e-02, 7.02769995e-01,
2.66652874e-04],
[ 2.96643794e-01, 2.00676220e-03, 7.01082468e-01,
2.67045165e-04]], dtype=float32),
array([[ 1.31966977e-03, 2.91907345e-05, 1.01504503e-08,
9.98651206e-01],
[ 1.17608532e-02, 9.88238931e-01, 8.18361912e-09,
2.57290282e-07],
[ 2.06351101e-01, 7.93406546e-01, 5.57995918e-05,
1.86489735e-04],
[ 2.52428532e-01, 7.47125268e-01, 3.08755873e-04,
1.37427691e-04]], dtype=float32),
[],
array([[ 3.55147233e-04, 3.45268927e-05, 5.26035243e-14,
9.99610364e-01],
[ 1.37195975e-01, 8.62191975e-01, 3.16499209e-05,
5.80396561e-04],
[ 1.39673322e-01, 8.60324502e-01, 2.00660770e-06,
6.05610850e-08],
[ 2.04938099e-01, 7.93173075e-01, 3.24997818e-04,
1.56381773e-03],
[ 1.64459705e-01, 4.54172902e-02, 1.00944417e-04,
7.90022135e-01],
[ 2.47133419e-01, 7.52043128e-01, 4.19590768e-04,
4.03887243e-04],
[ 2.46270955e-01, 7.52035737e-01, 6.10150106e-04,
1.08317297e-03],
[ 2.50155032e-01, 7.47876525e-01, 1.11321465e-03,
8.55259481e-04],
[ 2.55320609e-01, 7.42697001e-01, 9.94902221e-04,
9.87551524e-04],
[ 2.71485537e-01, 7.25024164e-01, 1.10378838e-03,
2.38647475e-03],
[ 2.77642578e-01, 7.18398809e-01, 6.76098454e-04,
3.28251347e-03],
[ 2.79663920e-01, 7.18179584e-01, 1.28760410e-03,
8.68913601e-04],
[ 2.80009300e-01, 7.11814642e-01, 6.37368858e-03,
1.80231675e-03],
[ 2.80500233e-01, 7.10099280e-01, 4.13475977e-03,
5.26575325e-03],
[ 2.75760770e-01, 7.03660011e-01, 3.02824564e-03,
1.75509304e-02],
[ 2.82357842e-01, 7.03274131e-01, 7.34727969e-03,
7.02073937e-03],
[ 2.57220507e-01, 7.00119317e-01, 4.60594048e-04,
4.21995483e-02]], dtype=float32),
array([[ 3.31386342e-03, 9.96638656e-01, 6.09745499e-09,
4.75125344e-05],
[ 2.26739451e-01, 7.73182333e-01, 3.17993763e-05,
4.63269389e-05]], dtype=float32),
array([[ 1.15070041e-04, 9.99884248e-01, 3.74500648e-12,
6.89272554e-07],
[ 2.60888547e-01, 7.33263910e-01, 2.24561105e-03,
3.60188400e-03]], dtype=float32),
...]
In [11]:
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = np.around(cm.astype('float') / cm.sum(axis=1), decimals=4)
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
In [5]:
def buildY(results):
y_true = []
y_pred = []
for i, k in enumerate(val_keys):
y_onehot = gt[k][:, 4:]
y = np.argmax(y_onehot, axis=1)
result = results[i]
if len(result) > 0:
pred = result[:, 0].astype(int)
pred -= 1 # label starts at 0, ignoring background label
size = min(len(pred), len(y))
y_true.extend(y[:size])
y_pred.extend(pred[:size])
return y_true, y_pred
def buildYWithProb(results, scores):
less_pred_count = 0
more_pred_count = 0
y_true = []
y_pred = []
y_scores = []
for i, k in enumerate(val_keys):
y_onehot = gt[k][:, 4:]
c = np.zeros((len(y_onehot), 1))
y_onehot = np.concatenate((c, y_onehot), axis=1) # add 0 for background
y = np.argmax(y_onehot, axis=1).tolist()
score = scores[i]
pred = results[i]
pred_length = len(pred)
y_length = len(y)
if pred_length > 0:
pred = pred[:, 0].astype(int)
if pred_length > y_length:
more_pred_count += 1
z = np.zeros(pred_length, dtype=int)
z[:y_length] = y
y = z
elif pred_length < y_length:
less_pred_count += 1
z = np.zeros(y_length, dtype=int)
z_scores = np.array([[0] * 4] * y_length, dtype=float)
if pred_length != 0:
z_scores[:pred_length] = score
z[:pred_length] = pred
score = z_scores
pred = z
y_true.extend(y)
y_pred.extend(pred)
y_scores.extend(score)
return y_true, y_pred, np.array(y_scores)
Confustion Matrix¶
In [13]:
results, scores = detection_out(pred_prob, keep_top_k=50, confidence_threshold=0.7)
print(len(results))
print(len(val_keys))
y_true, y_pred = buildY(results)
print('len(y_true)', len(y_true))
print('len(y_pred)', len(y_pred))
# print('y_true', y_true)
# print('y_pred', y_pred)
print('y_true', np.unique(y_true))
print('y_pred', np.unique(y_pred))
4437 4437 len(y_true) 20008 len(y_pred) 20008 y_true [0 1 2] y_pred [0 1 2]
In [15]:
# plt.figure()
cnf_matrix = confusion_matrix(y_true, y_pred)
plot_confusion_matrix(cnf_matrix, classes=['car', 'pedestrian', 'truck'],
title='Confusion matrix, without normalization')
plt.show()
Confusion matrix, without normalization [[15395 830 847] [ 1007 947 65] [ 629 62 226]]
In [16]:
results, scores = detection_out(pred_prob, keep_top_k=50, confidence_threshold=0.7)
y_true, y_pred, y_scores = buildYWithProb(results, scores)
print('len(y_scores)', len(y_scores))
print('len(y_true)', len(y_true))
print('len(y_pred)', len(y_pred))
# print('y_true', y_true)
# print('y_pred', y_pred)
# print('y_scores', np.array(y_scores))
print('y_true', np.unique(y_true))
print('y_pred', np.unique(y_pred))
len(y_scores) 38886 len(y_true) 38886 len(y_pred) 38886 y_true [0 1 2 3] y_pred [0 1 2 3]
In [18]:
cnf_matrix = confusion_matrix(y_true, y_pred)
plot_confusion_matrix(cnf_matrix, classes=['misclassified', 'car', 'pedestrian', 'truck'],
title='Confusion matrix, without normalization')
plt.show()
Confusion matrix, without normalization [[ 0 7751 1403 334] [ 7867 15395 830 847] [ 1001 1007 947 65] [ 522 629 62 226]]
In [19]:
plot_confusion_matrix(cnf_matrix, normalize=True, classes=['missclassified', 'car', 'pedestrian', 'truck'],
title='Confusion matrix, with normalization')
plt.show()
Normalized confusion matrix [[ 0. 0.3108 0.4646 0.2321] [ 0.8292 0.6173 0.2748 0.5886] [ 0.1055 0.0404 0.3136 0.0452] [ 0.055 0.0252 0.0205 0.1571]]
Precision, Recall and F1-Score¶
In [20]:
def apk(actual, predicted, k=10):
"""
Computes the average precision at k.
This function computes the average prescision at k between two lists of
items.
Parameters
----------
actual : list
A list of elements that are to be predicted (order doesn't matter)
predicted : list
A list of predicted elements (order does matter)
k : int, optional
The maximum number of predicted elements
Returns
-------
score : double
The average precision at k over the input lists
"""
if len(predicted)>k:
predicted = predicted[:k]
score = 0.0
num_hits = 0.0
for i,p in enumerate(predicted):
if p in actual and p not in predicted[:i]:
num_hits += 1.0
score += num_hits / (i+1.0)
if not actual:
return 0.0
return score / min(len(actual), k)
def mapk(actual, predicted, k=10):
"""
Computes the mean average precision at k.
This function computes the mean average prescision at k between two lists
of lists of items.
Parameters
----------
actual : list
A list of lists of elements that are to be predicted
(order doesn't matter in the lists)
predicted : list
A list of lists of predicted elements
(order matters in the lists)
k : int, optional
The maximum number of predicted elements
Returns
-------
score : double
The mean average precision at k over the input lists
"""
return np.mean([apk(a,p,k) for a,p in zip(actual, predicted)])
In [21]:
y_true = []
y_pred = []
for i, k in enumerate(val_keys[:2]):
y_onehot = gt[k][:, 4:]
c = np.zeros((len(y_onehot), 1))
y_onehot = np.concatenate((c, y_onehot), axis=1) # add 0 for background
y = np.argmax(y_onehot, axis=1).tolist()
pred = results[i]
pred_length = len(pred)
y_length = len(y)
if pred_length > 0:
pred = pred[:, 0].astype(int).tolist()
if pred_length > y_length:
z = np.zeros(pred_length, dtype=int)
z[:y_length] = y
y = z.tolist()
elif pred_length < y_length:
z = np.zeros(y_length, dtype=int)
z[:pred_length] = pred
pred = z
y_true.append(y)
y_pred.append(pred)
In [22]:
mapk(y_true, y_pred, k=10)
Out[22]:
0.1388888888888889
In [23]:
mapk(y_true, y_pred, k=5)
Out[23]:
0.20000000000000001
In [24]:
results, scores = detection_out(pred_prob, keep_top_k=50, confidence_threshold=0.7)
y_true, y_pred, _ = buildYWithProb(results, scores)
precision_score(y_true, y_pred, average='micro')
Out[24]:
0.42606593632669854
In [25]:
results, scores = detection_out(pred_prob, keep_top_k=50, confidence_threshold=0.7)
recall_score(y_true, y_pred, average='weighted')
# precision_recall_curve([1, 2, 1], [0, 0, 0], average='weighted') # take a look at this case
Out[25]:
0.42606593632669854
In [26]:
precision = dict()
recall = dict()
average_precision = dict()
y_true_onehot = np_utils.to_categorical(y_true, NUM_CLASSES)
for i in range(NUM_CLASSES):
precision[i], recall[i], _ = precision_recall_curve(y_true_onehot[:, i],
y_scores[:, i])
average_precision[i] = average_precision_score(y_true_onehot[:, i], y_scores[:, i])
# Compute micro-average ROC curve and ROC area
precision["micro"], recall["micro"], _ = precision_recall_curve(y_true_onehot.ravel(), y_scores.ravel())
average_precision["micro"] = average_precision_score(y_true_onehot, y_scores,
average="micro")
In [27]:
# Plot Precision-Recall curve
lw = 2
colors = itertools.cycle(['navy', 'turquoise', 'darkorange', 'cornflowerblue', 'teal'])
plt.clf()
plt.plot(recall[0], precision[0], lw=lw, color='navy',
label='Precision-Recall curve')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title('Precision-Recall example: AUC={0:0.2f}'.format(average_precision[0]))
plt.legend(loc="lower left")
plt.show()
# Plot Precision-Recall curve for each class
plt.clf()
plt.plot(recall["micro"], precision["micro"], color='gold', lw=lw,
label='micro-average Precision-recall curve (area = {0:0.2f})'
''.format(average_precision["micro"]))
for i, color in zip(range(NUM_CLASSES), colors):
plt.plot(recall[i], precision[i], color=color, lw=lw,
label='Precision-recall curve of class {0} (area = {1:0.2f})'
''.format(i, average_precision[i]))
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Extension of Precision-Recall curve to multi-class')
plt.legend(loc="lower right")
plt.show()
In [28]:
print(classification_report(y_true, y_pred, target_names=['misclassfied', 'car', 'pedestrian', 'truck']))
precision recall f1-score support
misclassfied 0.00 0.00 0.00 9488
car 0.62 0.62 0.62 24939
pedestrian 0.29 0.31 0.30 3020
truck 0.15 0.16 0.16 1439
avg / total 0.43 0.43 0.43 38886
In [29]:
y_true_onehot = np_utils.to_categorical(y_true, NUM_CLASSES)
roc_auc_score(y_true_onehot, y_scores)
Out[29]:
0.62398537619475536
Predictions¶
In [29]:
def build_inputs(keys):
inputs = []
images = []
path_prefix = 'resources/udacity-dataset/'
for key in keys:
img_path = path_prefix + key
img = image.load_img(img_path, target_size=(300, 300))
img = image.img_to_array(img)
images.append(imread(img_path))
inputs.append(img.copy())
inputs = preprocess_input(np.array(inputs))
return inputs, images
def plot(inputs, images):
preds = model.predict(inputs, batch_size=1, verbose=1)
results = bbox_util.detection_out(preds)
for i, img in enumerate(images):
# Parse the outputs.
det_label = results[i][:, 0]
det_conf = results[i][:, 1]
det_xmin = results[i][:, 2]
det_ymin = results[i][:, 3]
det_xmax = results[i][:, 4]
det_ymax = results[i][:, 5]
# Get detections with confidence higher than 0.6.
top_indices = [i for i, conf in enumerate(det_conf) if conf >= 0.9]
top_conf = det_conf[top_indices]
top_label_indices = det_label[top_indices].tolist()
top_xmin = det_xmin[top_indices]
top_ymin = det_ymin[top_indices]
top_xmax = det_xmax[top_indices]
top_ymax = det_ymax[top_indices]
colors = plt.cm.hsv(np.linspace(0, 1, 21)).tolist()
plt.imshow(img / 255.)
currentAxis = plt.gca()
for i in range(top_conf.shape[0]):
xmin = int(round(top_xmin[i] * img.shape[1]))
ymin = int(round(top_ymin[i] * img.shape[0]))
xmax = int(round(top_xmax[i] * img.shape[1]))
ymax = int(round(top_ymax[i] * img.shape[0]))
score = top_conf[i]
label = int(top_label_indices[i])
# label_name = CLASSES[label - 1]
label_name = label - 1
display_txt = '{:0.2f}, {}'.format(score, label_name)
coords = (xmin, ymin), xmax-xmin+1, ymax-ymin+1
color = colors[label]
currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2))
currentAxis.text(xmin, ymin, display_txt, bbox={'facecolor':color, 'alpha':0.5})
plt.show()
In [11]:
input_shape=(300, 300, 3)
NUM_CLASSES = 3
model = SSD300(input_shape, num_classes=NUM_CLASSES)
model.load_weights('weights.48-1.13.hdf5', by_name=True)
bbox_util = BBoxUtility(NUM_CLASSES, priors)
In [30]:
keys = ['object-detection-crowdai/1479505369437332886.jpg']
inputs, images = build_inputs(keys)
plot(inputs, images)
1/1 [==============================] - 1s
In [31]:
keys = ['object-detection-crowdai/1479502217225257061.jpg', 'object-detection-crowdai/1479498371963069978.jpg',
'object-detection-crowdai/1479503036282378933.jpg', 'object-dataset/1478019952686311006.jpg', 'object-dataset/1478019953689774621.jpg',
'object-dataset/1478019955185244088.jpg', 'object-dataset/1478019959681353555.jpg', 'object-dataset/1478901535246276321.jpg',
'object-dataset/1478901536388465963.jpg', 'object-dataset/1478901532389636298.jpg']
inputs, images = build_inputs(keys)
plot(inputs, images)
10/10 [==============================] - 17s
In [32]:
from random import shuffle
keys = sorted(gt.keys())
shuffle(keys)
num_train = int(round(0.8 * len(keys)))
train_keys = keys[:num_train]
val_keys = keys[num_train:]
In [33]:
inputs = []
images = []
keys = list(map(lambda i: val_keys[i], range(60, 70)))
print(list(keys))
inputs, images = build_inputs(keys)
plot(inputs, images)
['object-dataset/1478899855082761665.jpg', 'object-detection-crowdai/1479504330366436549.jpg', 'object-detection-crowdai/1479501729692847065.jpg', 'object-dataset/1478898008674085287.jpg', 'object-dataset/1478898385726303289.jpg', 'object-dataset/1478896925509187138.jpg', 'object-dataset/1478895331611742960.jpg', 'object-dataset/1478900828560398777.jpg', 'object-detection-crowdai/1479504224862874415.jpg', 'object-dataset/1478897031769090925.jpg'] 10/10 [==============================] - 17s
In [27]:
import matplotlib.image as mpimg
def plot_gt(images, gt=[]):
for i, img in enumerate(images):
for row in gt:
xmin = int(round(row[0] * img.shape[1]))
ymin = int(round(row[1] * img.shape[0]))
xmax = int(round(row[2] * img.shape[1]))
ymax = int(round(row[3] * img.shape[0]))
cv2.rectangle(img, (xmin, ymin), (xmax, ymax),(0, 255, 0), 3)
plt.imshow(img / 255.)
plt.show()
def plot_pred(inputs, images, gt=[]):
preds = model.predict(inputs, batch_size=1, verbose=1)
results = bbox_util.detection_out(preds)
print(results)
for i, img in enumerate(images):
# Parse the outputs.
det_label = results[i][:, 0]
det_conf = results[i][:, 1]
det_xmin = results[i][:, 2]
det_ymin = results[i][:, 3]
det_xmax = results[i][:, 4]
det_ymax = results[i][:, 5]
# Get detections with confidence higher than 0.6.
top_indices = [i for i, conf in enumerate(det_conf) if conf >= 0.7]
top_conf = det_conf[top_indices]
top_label_indices = det_label[top_indices].tolist()
top_xmin = det_xmin[top_indices]
top_ymin = det_ymin[top_indices]
top_xmax = det_xmax[top_indices]
top_ymax = det_ymax[top_indices]
colors = plt.cm.hsv(np.linspace(0, 1, 21)).tolist()
currentAxis = plt.gca()
# print('len', len(pred), len(top_conf))
for i in range(top_conf.shape[0]):
xmin = int(round(top_xmin[i] * img.shape[1]))
ymin = int(round(top_ymin[i] * img.shape[0]))
xmax = int(round(top_xmax[i] * img.shape[1]))
ymax = int(round(top_ymax[i] * img.shape[0]))
score = top_conf[i]
label = int(top_label_indices[i])
label_name = CLASSES[label - 1]
display_txt = '{:0.2f}, {}'.format(score, label_name)
coords = (xmin, ymin), xmax-xmin+1, ymax-ymin+1
color = colors[label]
currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2))
currentAxis.text(xmin, ymin, display_txt, bbox={'facecolor':color, 'alpha':0.5})
# for row in gt:
# xmin = int(round(row[0] * img.shape[1]))
# ymin = int(round(row[1] * img.shape[0]))
# xmax = int(round(row[2] * img.shape[1]))
# ymax = int(round(row[3] * img.shape[0]))
# cv2.rectangle(img, (xmin, ymin), (xmax, ymax),(0, 255, 0), 3)
plt.imshow(img / 255.)
plt.show()
In [28]:
k = val_keys[:1][0]
inputs, images = build_inputs(val_keys[:1])
plot_pred(inputs, images, gt[k])
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-28-ff7b4752f574> in <module>() ----> 1 k = val_keys[:1][0] 2 inputs, images = build_inputs(val_keys[:1]) 3 plot_pred(inputs, images, gt[k]) NameError: name 'val_keys' is not defined
In [29]:
keys = ['object-detection-crowdai/1479502217225257061.jpg']
inputs, images = build_inputs(keys)
plot_pred(inputs, images, gt[keys[0]])
1/1 [==============================] - 0s
[array([[ 1. , 0.02490502, 0.38178486, 0.4577885 , 0.40496916,
0.51017106],
[ 2. , 0.02068295, 0.20439565, 0.44510025, 0.25658837,
0.50228137],
[ 2. , 0.02050075, 0.38178486, 0.4577885 , 0.40496916,
0.51017106],
[ 2. , 0.01803392, 0.05625729, 0.41930705, 0.1334022 ,
0.55478305],
[ 1. , 0.01787028, 0.30527326, 0.44421276, 0.35373172,
0.50377852],
[ 1. , 0.0174972 , 0.25608921, 0.46404237, 0.27991408,
0.50712681],
[ 2. , 0.01735546, 0.24679573, 0.46797958, 0.27133784,
0.49959889],
[ 1. , 0.01717375, 0.71129847, 0.44679239, 0.74473476,
0.49469402],
[ 1. , 0.01702988, 0.24679573, 0.46797958, 0.27133784,
0.49959889],
[ 2. , 0.0168166 , 0.26963532, 0.46417224, 0.3222 ,
0.5194906 ],
[ 2. , 0.0163738 , 0.07875793, 0.43080926, 0.16117604,
0.53672981],
[ 2. , 0.0161798 , 0.31012392, 0.45584342, 0.36143935,
0.51471263],
[ 1. , 0.01612418, 0.16644378, 0.45475411, 0.2081009 ,
0.51395851],
[ 2. , 0.01610016, 0.25608921, 0.46404237, 0.27991408,
0.50712681],
[ 1. , 0.01543052, 0.21533638, 0.45722827, 0.26026788,
0.50664121],
[ 2. , 0.01530703, 0.28368929, 0.45038807, 0.32620755,
0.49965894],
[ 2. , 0.01526676, 0.21480435, 0.43624082, 0.24633923,
0.47412595],
[ 1. , 0.01516208, 0.25918749, 0.45295027, 0.28317586,
0.49210939],
[ 2. , 0.01491207, 0.15326253, 0.4567613 , 0.19176129,
0.49857706],
[ 1. , 0.01488656, 0.68417603, 0.46312013, 0.72846848,
0.49514607],
[ 2. , 0.0147663 , 0.13267601, 0.44641775, 0.17554563,
0.51641589],
[ 1. , 0.01475651, 0.15326253, 0.4567613 , 0.19176129,
0.49857706],
[ 2. , 0.01462046, 0.24712488, 0.43856236, 0.2708663 ,
0.47165295],
[ 1. , 0.01459685, 0.59279925, 0.47266111, 0.6206494 ,
0.51591545],
[ 2. , 0.01451528, 0.16774786, 0.44290251, 0.20653808,
0.49926251],
[ 1. , 0.0143237 , 0.20185189, 0.4667519 , 0.23095162,
0.50253791],
[ 2. , 0.01413491, 0.23863921, 0.45008272, 0.27084598,
0.49605346],
[ 1. , 0.01387826, 0.17780359, 0.43501332, 0.20727809,
0.47833869],
[ 1. , 0.01383122, 0.23863921, 0.45008272, 0.27084598,
0.49605346],
[ 1. , 0.0136822 , 0.21480435, 0.43624082, 0.24633923,
0.47412595],
[ 2. , 0.01355646, 0.26093608, 0.46128756, 0.29908621,
0.51556206],
[ 1. , 0.01346515, 0.05625729, 0.41930705, 0.1334022 ,
0.55478305],
[ 2. , 0.01332042, 0.25918749, 0.45295027, 0.28317586,
0.49210939],
[ 2. , 0.01324153, 0.59279925, 0.47266111, 0.6206494 ,
0.51591545],
[ 2. , 0.01322293, 0.80773461, 0.49413574, 1. ,
0.88612294],
[ 1. , 0.01300691, 0.36142299, 0.46130827, 0.38771698,
0.51028514],
[ 2. , 0.01286438, 0.71129847, 0.44679239, 0.74473476,
0.49469402],
[ 2. , 0.01276894, 0.35657585, 0.45765933, 0.38961649,
0.51372594],
[ 1. , 0.01273654, 0.07595192, 0.42742339, 0.16294718,
0.52320546],
[ 2. , 0.01240511, 0.20185189, 0.4667519 , 0.23095162,
0.50253791],
[ 2. , 0.0122796 , 0.20405333, 0.4496831 , 0.22854619,
0.48265132],
[ 2. , 0.0122485 , 0.17780359, 0.43501332, 0.20727809,
0.47833869],
[ 2. , 0.01207699, 0.17999513, 0.46464467, 0.21103285,
0.50290781],
[ 1. , 0.01197179, 0.28368929, 0.45038807, 0.32620755,
0.49965894],
[ 2. , 0.01196762, 0.27303937, 0.46878457, 0.29976335,
0.49902737],
[ 1. , 0.01192003, 0.26963532, 0.46417224, 0.3222 ,
0.5194906 ],
[ 1. , 0.01183826, 0.80773461, 0.49413574, 1. ,
0.88612294],
[ 2. , 0.01181237, 0.7255317 , 0.43553412, 0.75136805,
0.50459081],
[ 1. , 0.01174062, 0.25863558, 0.47441244, 0.28497332,
0.52342451],
[ 1. , 0.01143914, 0.13267601, 0.44641775, 0.17554563,
0.51641589],
[ 2. , 0.01143057, 0.40793791, 0.43708351, 0.4679037 ,
0.52933425],
[ 1. , 0.01122626, 0.26093608, 0.46128756, 0.29908621,
0.51556206],
[ 1. , 0.01111475, 0.65688318, 0.47254696, 0.68331105,
0.50572383],
[ 2. , 0.01098022, 0.61819744, 0.47340706, 0.6429534 ,
0.51199532],
[ 1. , 0.01059118, 0.63844037, 0.46888161, 0.65905833,
0.50070709],
[ 1. , 0.01046625, 0.17999513, 0.46464467, 0.21103285,
0.50290781],
[ 1. , 0.01036723, 0.24712488, 0.43856236, 0.2708663 ,
0.47165295],
[ 1. , 0.0100184 , 0.7255317 , 0.43553412, 0.75136805,
0.50459081]])]
In [12]:
def iou(boxA, boxB):
# determine the (x, y)-coordinates of the intersection rectangle
xA = max(boxA[0], boxB[0])
yA = max(boxA[1], boxB[1])
xB = min(boxA[2], boxB[2])
yB = min(boxA[3], boxB[3])
# print('xA', xA)
# print('yA', yA)
# print('xB', xB)
# print('yB', yB)
epsilon = 1e-8
# compute the area of intersection rectangle
x = (xB - xA + epsilon)
y = (yB - yA + epsilon)
if x <= epsilon or y <= epsilon:
interArea = 0
else:
interArea = x * y
# print('interArea', interArea)
# compute the area of both the prediction and ground-truth
# rectangles
boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
# print('boxAArea', boxAArea)
# print('boxBArea', boxBArea)
# compute the intersection over union by taking the intersection
# area and dividing it by the sum of prediction + ground-truth
# areas - the interesection area
iou = interArea / float(boxAArea + boxBArea - interArea)
# return the intersection over union value
return iou
def denormalize(val, w, h):
return (val[0]*w, val[1]*h, val[2]*w, val[3]*h)
boxA = (0, 0, 1, 1)
boxB = (0.5, 0.5, 1.5, 1.5)
iou(boxA, boxB)
Out[12]:
0.032258065848074946
In [150]:
def measure(keys):
# predict
inputs, images = build_inputs(keys)
preds = model.predict(inputs, batch_size=1, verbose=1)
results = bbox_util.detection_out(preds, keep_top_k=50, confidence_threshold=0.7)
for i, k in enumerate(keys):
TP = 0
FP = 0
FN = 0
img = images[i]
y_onehot = gt[k]
print('key', k)
print('y_onehot count', len(y_onehot))
print('pred count', len(results[i]))
if len(results[i]) == 0:
pred_bb = []
y_prob = []
y_pred = []
else:
pred_bb = results[i][:, 2:]
y_prob = results[i][:, 1]
y_pred = results[i][:, 0].astype(int) - 1 # -1 because we ignore background class, so class 0 becomes car
gt_bb = gt[k][:, :4]
y_gt = np.argmax(gt[k][:, 4:], axis=1)
FN += abs(len(y_pred) - len(y_gt))
d = dict()
for j in range(len(pred_bb)):
prob = y_prob[j]
label = CLASSES[y_pred[j]]
pred_bb_dnorm = denormalize(pred_bb[j], img.shape[1], img.shape[0])
for k in range(len(gt_bb)):
gt_bb_dnorm = denormalize(gt_bb[k], img.shape[1], img.shape[0])
iou_val = iou(gt_bb_dnorm, pred_bb_dnorm)
if iou_val > 0:
#(j, k): j=prediction index, k=groundtruth index
d[(j, k)] = iou_val
d_sorted = sorted(d.items(), key=operator.itemgetter(1), reverse=True)
gt_detected = set()
pred_detected = set()
for k, v in d_sorted:
print(k, v)
print('---------------------------')
for k, v in d_sorted:
pred_idx, gt_idx = k
if gt_idx not in gt_detected:
if pred_idx not in pred_detected:
print('y_gt', y_gt[gt_idx])
print('y_pred', y_pred[pred_idx])
if y_gt[gt_idx] == y_pred[pred_idx]:
TP += 1
else:
FP += 1
print(gt_idx, 'is selected with pred', pred_idx)
gt_detected.add(gt_idx)
pred_detected.add(pred_idx)
elif pred_idx not in pred_detected:
FP += 1
print('No pred', pred_idx, 'but groundtruth', gt_idx, 'is detected already', (k, v))
print('TP:', TP)
print('FP:', FP)
print('FN:', FN)
In [11]:
keys = ['object-dataset/1478895407021175056.jpg']
inputs, images = build_inputs(keys)
plot_gt(images, gt[keys[0]])
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-11-43d684b2394d> in <module>() 1 keys = ['object-dataset/1478895407021175056.jpg'] 2 inputs, images = build_inputs(keys) ----> 3 plot_gt(images, gt[keys[0]]) NameError: name 'plot_gt' is not defined
In [ ]:
inputs, images = build_inputs(keys)
plot_pred(inputs, images, gt[keys[0]])
In [153]:
measure(keys)
1/1 [==============================] - 0s key object-dataset/1478895407021175056.jpg y_onehot count 2 pred count 0 --------------------------- TP: 0 FP: 0 FN: 2
In [154]:
keys = ['object-detection-crowdai/1479502217225257061.jpg']
inputs, images = build_inputs(keys)
plot_gt(images, gt[keys[0]])
In [155]:
inputs, images = build_inputs(keys)
plot_pred(inputs, images, gt[keys[0]])
1/1 [==============================] - 0s
In [156]:
measure(keys)
1/1 [==============================] - 0s key object-detection-crowdai/1479502217225257061.jpg y_onehot count 7 pred count 13 (0, 1) 0.966048340506 (2, 2) 0.947932123246 (1, 0) 0.947920408764 (7, 4) 0.71115073732 (11, 6) 0.643504822877 (4, 5) 0.52185697641 (10, 6) 0.446814122306 (12, 5) 0.434054680646 (10, 3) 0.398766075794 (8, 3) 0.391241391557 (9, 4) 0.238590909391 (3, 1) 0.203118489094 (11, 3) 0.151521191432 (5, 1) 0.0844548812529 (6, 5) 0.0600402047351 (4, 1) 0.0450520727018 (0, 5) 0.0413319458603 (12, 1) 0.0282531039064 (6, 1) 0.0239489851156 (8, 6) 0.022757207259 (0, 4) 0.0110707868412 (11, 0) 0.00709974166082 (7, 1) 0.0061913617531 (1, 6) 0.00561904182386 (10, 0) 0.000647340242498 (7, 5) 0.000116209984543 --------------------------- y_gt 0 y_pred 0 1 is selected with pred 0 y_gt 0 y_pred 0 2 is selected with pred 2 y_gt 0 y_pred 0 0 is selected with pred 1 y_gt 0 y_pred 0 4 is selected with pred 7 y_gt 0 y_pred 0 6 is selected with pred 11 y_gt 0 y_pred 0 5 is selected with pred 4 No pred 10 but groundtruth 6 is detected already ((10, 6), 0.44681412230590312) No pred 12 but groundtruth 5 is detected already ((12, 5), 0.43405468064604286) y_gt 0 y_pred 0 3 is selected with pred 10 No pred 8 but groundtruth 3 is detected already ((8, 3), 0.39124139155653714) No pred 9 but groundtruth 4 is detected already ((9, 4), 0.2385909093910957) No pred 3 but groundtruth 1 is detected already ((3, 1), 0.20311848909383992) No pred 5 but groundtruth 1 is detected already ((5, 1), 0.084454881252859243) No pred 6 but groundtruth 5 is detected already ((6, 5), 0.060040204735078109) No pred 12 but groundtruth 1 is detected already ((12, 1), 0.028253103906371833) No pred 6 but groundtruth 1 is detected already ((6, 1), 0.023948985115621893) No pred 8 but groundtruth 6 is detected already ((8, 6), 0.022757207259023686) TP: 7 FP: 10 FN: 6
In [131]:
keys = ['object-dataset/1478896783257516407.jpg']
inputs, images = build_inputs(keys)
plot_gt(images, gt[keys[0]])
In [132]:
inputs, images = build_inputs(keys)
plot_pred(inputs, images, gt[keys[0]])
1/1 [==============================] - 0s
In [133]:
measure(['object-dataset/1478896783257516407.jpg'])
1/1 [==============================] - 0s key object-dataset/1478896783257516407.jpg y_onehot count 7 pred count 2 (0, 4) 0.936556049486 (1, 5) 0.745590402522 (1, 6) 0.102353660188 --------------------------- 4 is selected with pred 0 5 is selected with pred 1 TP: 2 FP: 0 FN: 5
In [157]:
keys = ['object-detection-crowdai/1479505929977588157.jpg']
inputs, images = build_inputs(keys)
plot_gt(images, gt[keys[0]])
In [158]:
inputs, images = build_inputs(keys)
plot_pred(inputs, images, gt[keys[0]])
1/1 [==============================] - 0s
In [159]:
measure(keys)
1/1 [==============================] - 0s key object-detection-crowdai/1479505929977588157.jpg y_onehot count 10 pred count 8 (0, 0) 0.896091949116 (1, 3) 0.895897731457 (3, 7) 0.852828778435 (2, 4) 0.78597713948 (6, 1) 0.703358122535 (5, 6) 0.488214778116 (4, 5) 0.45637956249 (5, 5) 0.324520043153 (7, 4) 0.289733792263 (4, 4) 0.217227331897 (7, 3) 0.206427305533 (3, 2) 0.128777009722 (7, 5) 0.0704735308146 (2, 3) 0.0694514979819 (4, 6) 0.0618401841906 (1, 4) 0.0489896980526 (2, 5) 0.0379435560218 --------------------------- y_gt 0 y_pred 0 0 is selected with pred 0 y_gt 0 y_pred 0 3 is selected with pred 1 y_gt 2 y_pred 2 7 is selected with pred 3 y_gt 0 y_pred 0 4 is selected with pred 2 y_gt 0 y_pred 0 1 is selected with pred 6 y_gt 0 y_pred 0 6 is selected with pred 5 y_gt 0 y_pred 0 5 is selected with pred 4 No pred 7 but groundtruth 4 is detected already ((7, 4), 0.28973379226274387) No pred 7 but groundtruth 3 is detected already ((7, 3), 0.20642730553278024) No pred 7 but groundtruth 5 is detected already ((7, 5), 0.070473530814591803) TP: 7 FP: 3 FN: 2
In [137]:
measure(np.array(val_keys)[range(40, 50)])
8/10 [=======================>......] - ETA: 0skey object-dataset/1478020633205868287.jpg y_onehot count 1 pred count 1 (0, 0) 0.827113541685 --------------------------- 0 is selected with pred 0 TP: 1 FP: 0 FN: 0 key object-dataset/1478901476974216608.jpg y_onehot count 10 pred count 1 (0, 1) 0.774275215927 (0, 0) 0.703161999527 --------------------------- 1 is selected with pred 0 TP: 1 FP: 0 FN: 9 key object-dataset/1478895407021175056.jpg y_onehot count 2 pred count 0 --------------------------- TP: 0 FP: 0 FN: 2 key object-dataset/1478899543158243104.jpg y_onehot count 11 pred count 28 (0, 9) 0.837127843591 (3, 10) 0.79732208224 (5, 6) 0.753543752438 (11, 5) 0.748687800582 (4, 1) 0.725387214878 (7, 2) 0.711021505947 (18, 4) 0.707284133095 (2, 0) 0.666875668095 (8, 7) 0.633950024231 (10, 7) 0.547698793054 (1, 8) 0.546575836055 (6, 3) 0.545067914474 (7, 3) 0.533641284661 (17, 10) 0.495404982166 (10, 8) 0.462782460731 (25, 3) 0.461131120499 (12, 0) 0.435983457719 (14, 1) 0.407271992435 (26, 9) 0.40663777222 (6, 4) 0.387937621038 (15, 8) 0.330200382529 (13, 1) 0.324211026232 (23, 1) 0.321888534488 (13, 0) 0.296961829348 (6, 2) 0.291748808895 (19, 3) 0.289590358698 (20, 1) 0.28772002374 (24, 8) 0.287325189007 (16, 0) 0.274394335953 (25, 2) 0.256098287784 (4, 2) 0.248085070756 (16, 1) 0.244725313834 (27, 8) 0.21252510053 (20, 2) 0.210244175178 (20, 3) 0.204057973582 (19, 4) 0.201226688056 (23, 2) 0.199642470543 (19, 2) 0.190727903744 (7, 1) 0.188666801079 (22, 9) 0.186449062308 (8, 8) 0.185160082544 (21, 3) 0.183426759602 (1, 7) 0.175734590767 (9, 9) 0.165979780742 (21, 4) 0.162729757519 (20, 4) 0.154423016395 (1, 9) 0.152447242628 (25, 4) 0.136613756915 (14, 2) 0.132023045217 (23, 3) 0.131733471017 (16, 2) 0.126695140326 (21, 2) 0.118993404379 (15, 7) 0.114541049259 (27, 9) 0.100543425305 (13, 2) 0.0979120210377 (4, 3) 0.0938562066866 (24, 9) 0.0860460114945 (15, 9) 0.0794852110396 (16, 3) 0.0781720996081 (2, 1) 0.0704017612509 (22, 8) 0.0577699794135 (14, 0) 0.055805604465 (26, 10) 0.0555578054747 (21, 5) 0.0534882405526 (12, 1) 0.0505797486246 (25, 1) 0.0458190260437 (18, 3) 0.0343560483734 (9, 8) 0.0289760510739 (24, 7) 0.027951507555 (14, 3) 0.0274636611421 (20, 0) 0.0262087700997 (0, 8) 0.0189554067972 (4, 0) 0.0148228892678 (21, 1) 0.00937077519508 (13, 3) 0.00793289072856 (6, 1) 0.00643213919531 --------------------------- 9 is selected with pred 0 10 is selected with pred 3 6 is selected with pred 5 5 is selected with pred 11 1 is selected with pred 4 2 is selected with pred 7 4 is selected with pred 18 0 is selected with pred 2 7 is selected with pred 8 No pred 10 but groundtruth 7 is detected already ((10, 7), 0.5476987930541708) 8 is selected with pred 1 3 is selected with pred 6 No pred 17 but groundtruth 10 is detected already ((17, 10), 0.49540498216637607) No pred 10 but groundtruth 8 is detected already ((10, 8), 0.46278246073148926) No pred 25 but groundtruth 3 is detected already ((25, 3), 0.46113112049948168) No pred 12 but groundtruth 0 is detected already ((12, 0), 0.43598345771877084) No pred 14 but groundtruth 1 is detected already ((14, 1), 0.40727199243492168) No pred 26 but groundtruth 9 is detected already ((26, 9), 0.4066377722198451) No pred 15 but groundtruth 8 is detected already ((15, 8), 0.33020038252862444) No pred 13 but groundtruth 1 is detected already ((13, 1), 0.32421102623208237) No pred 23 but groundtruth 1 is detected already ((23, 1), 0.32188853448750865) No pred 13 but groundtruth 0 is detected already ((13, 0), 0.2969618293478602) No pred 19 but groundtruth 3 is detected already ((19, 3), 0.2895903586975434) No pred 20 but groundtruth 1 is detected already ((20, 1), 0.28772002373964378) No pred 24 but groundtruth 8 is detected already ((24, 8), 0.28732518900694026) No pred 16 but groundtruth 0 is detected already ((16, 0), 0.27439433595342733) No pred 25 but groundtruth 2 is detected already ((25, 2), 0.25609828778375038) No pred 16 but groundtruth 1 is detected already ((16, 1), 0.24472531383415858) No pred 27 but groundtruth 8 is detected already ((27, 8), 0.21252510053006768) No pred 20 but groundtruth 2 is detected already ((20, 2), 0.21024417517809271) No pred 20 but groundtruth 3 is detected already ((20, 3), 0.20405797358159944) No pred 19 but groundtruth 4 is detected already ((19, 4), 0.20122668805606636) No pred 23 but groundtruth 2 is detected already ((23, 2), 0.19964247054258139) No pred 19 but groundtruth 2 is detected already ((19, 2), 0.19072790374395571) No pred 22 but groundtruth 9 is detected already ((22, 9), 0.18644906230798158) No pred 21 but groundtruth 3 is detected already ((21, 3), 0.18342675960236945) No pred 9 but groundtruth 9 is detected already ((9, 9), 0.16597978074185998) No pred 21 but groundtruth 4 is detected already ((21, 4), 0.16272975751940005) No pred 20 but groundtruth 4 is detected already ((20, 4), 0.15442301639459066) No pred 25 but groundtruth 4 is detected already ((25, 4), 0.13661375691526939) No pred 14 but groundtruth 2 is detected already ((14, 2), 0.13202304521703109) No pred 23 but groundtruth 3 is detected already ((23, 3), 0.13173347101652269) No pred 16 but groundtruth 2 is detected already ((16, 2), 0.12669514032613072) No pred 21 but groundtruth 2 is detected already ((21, 2), 0.11899340437877977) No pred 15 but groundtruth 7 is detected already ((15, 7), 0.11454104925875712) No pred 27 but groundtruth 9 is detected already ((27, 9), 0.10054342530460152) No pred 13 but groundtruth 2 is detected already ((13, 2), 0.097912021037662278) No pred 24 but groundtruth 9 is detected already ((24, 9), 0.086046011494503971) No pred 15 but groundtruth 9 is detected already ((15, 9), 0.079485211039643552) No pred 16 but groundtruth 3 is detected already ((16, 3), 0.078172099608092141) No pred 22 but groundtruth 8 is detected already ((22, 8), 0.057769979413466455) No pred 14 but groundtruth 0 is detected already ((14, 0), 0.05580560446496368) No pred 26 but groundtruth 10 is detected already ((26, 10), 0.055557805474728904) No pred 21 but groundtruth 5 is detected already ((21, 5), 0.053488240552633133) No pred 12 but groundtruth 1 is detected already ((12, 1), 0.05057974862456692) No pred 25 but groundtruth 1 is detected already ((25, 1), 0.045819026043650984) No pred 9 but groundtruth 8 is detected already ((9, 8), 0.028976051073855962) No pred 24 but groundtruth 7 is detected already ((24, 7), 0.027951507554965442) No pred 14 but groundtruth 3 is detected already ((14, 3), 0.027463661142118796) No pred 20 but groundtruth 0 is detected already ((20, 0), 0.026208770099734685) No pred 21 but groundtruth 1 is detected already ((21, 1), 0.0093707751950848021) No pred 13 but groundtruth 3 is detected already ((13, 3), 0.0079328907285575404) TP: 11 FP: 51 FN: 17 key object-detection-crowdai/1479505929977588157.jpg y_onehot count 10 pred count 8 (0, 0) 0.896091949116 (1, 3) 0.895897731457 (3, 7) 0.852828778435 (2, 4) 0.78597713948 (6, 1) 0.703358122535 (5, 6) 0.488214778116 (4, 5) 0.456379684428 (5, 5) 0.324520043153 (7, 4) 0.289733792263 (4, 4) 0.217227407976 (7, 3) 0.206427305533 (3, 2) 0.128777009722 (7, 5) 0.0704735308146 (2, 3) 0.0694514979819 (4, 6) 0.0618401646764 (1, 4) 0.0489896980526 (2, 5) 0.0379435560218 --------------------------- 0 is selected with pred 0 3 is selected with pred 1 7 is selected with pred 3 4 is selected with pred 2 1 is selected with pred 6 6 is selected with pred 5 5 is selected with pred 4 No pred 7 but groundtruth 4 is detected already ((7, 4), 0.28973379226274387) No pred 7 but groundtruth 3 is detected already ((7, 3), 0.20642730553278024) No pred 7 but groundtruth 5 is detected already ((7, 5), 0.070473530814591803) TP: 7 FP: 3 FN: 2 key object-dataset/1478732892144755790.jpg y_onehot count 9 pred count 4 (2, 0) 0.954675323142 (0, 2) 0.856498712659 (3, 6) 0.850330178219 (1, 3) 0.820630705644 (3, 7) 0.532188834541 (2, 1) 0.0294396432635 --------------------------- 0 is selected with pred 2 2 is selected with pred 0 6 is selected with pred 3 3 is selected with pred 1 TP: 4 FP: 0 FN: 5 key object-dataset/1478021874581175763.jpg y_onehot count 6 pred count 2 (1, 5) 0.861013576564 (0, 0) 0.785027239209 (1, 4) 0.00465877514385 --------------------------- 5 is selected with pred 1 0 is selected with pred 0 TP: 2 FP: 0 FN: 4 key object-dataset/1478901002804318543.jpg y_onehot count 4 pred count 1 (0, 0) 0.922856692537 --------------------------- 0 is selected with pred 0 TP: 1 FP: 0 FN: 3 key object-dataset/1478899202099077483.jpg y_onehot count 11 pred count 2 (1, 2) 0.972894553415 (0, 7) 0.830142392802 (0, 4) 0.0790074745015 (0, 5) 0.0308050222818 (1, 1) 0.0269206825106 (0, 8) 0.0210843111947 (0, 6) 0.0145955685019 --------------------------- 2 is selected with pred 1 7 is selected with pred 0 TP: 2 FP: 0 FN: 9 key object-dataset/1478732037652442251.jpg y_onehot count 2 pred count 1 --------------------------- TP: 0 FP: 0 FN: 1
In [138]:
keys = ['object-dataset/1478021874581175763.jpg']
inputs, images = build_inputs(keys)
plot_gt(images, gt[keys[0]])
In [139]:
inputs, images = build_inputs(keys)
plot_pred(inputs, images, gt[keys[0]])
1/1 [==============================] - 0s
In [140]:
measure(keys)
1/1 [==============================] - 0s key object-dataset/1478021874581175763.jpg y_onehot count 6 pred count 2 (1, 5) 0.861013576564 (0, 0) 0.785027239209 (1, 4) 0.00465877514385 --------------------------- 5 is selected with pred 1 0 is selected with pred 0 TP: 2 FP: 0 FN: 4
In [89]:
In [13]:
iou((1551.7339324951172, 744.94636058807373, 1781.6526031494141, 1064.3930196762085), (504.0, 569.0, 561.0, 627.0))
Out[13]:
0.0
In [ ]: